Deep Learning based Object Detection via Style-transferred Underwater Sonar Images

被引:18
|
作者
Lee, Sejin [1 ]
Park, Byungjae [2 ]
Kim, Ayoung [3 ]
机构
[1] Kongju Natl Univ, 1223-24 Cheonan Daero, Cheonan 31080, South Korea
[2] ETRI, 218 Gajeong Ro, Daejeon 34129, South Korea
[3] Korea Adv Inst Sci & Technol, Dept Civil & Environm Engn, Daejeon, South Korea
来源
IFAC PAPERSONLINE | 2019年 / 52卷 / 21期
关键词
D O I
10.1016/j.ifacol.2019.12.299
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Compared to the flourishing researches on terrestrial optical images, deep learning in underwater imaging has not been highlighted. Although some approaches applied deep learning in their underwater imaging still no major application has been found in underwater sonar imaging. Notably, the fundamental limitation in underwater image data would be the main cause of the bottleneck. To alleviate this issue, this paper introduces a simulation-generated dataset for object detection in underwater sonar images. Specifically, this paper focuses on generating real sonarlike style-transferred synthetic sonar images for network training. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:152 / 155
页数:4
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